{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bagging and Pasting\n",
    "Decision Tree ensemble is trained on feature set 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored variables and their in-db values:\n",
      "X_16_val                  -> array([[ 0.10924883,  1.83030605, -0.14807631, ...\n",
      "X_32_val                  -> array([[ 0.66944195,  0.46536115,  0.79919788, ...\n",
      "X_32test_std              -> defaultdict(<class 'list'>, {0: array([[ 0.6694419\n",
      "X_32train_std             -> array([[-0.74031227,  0.0126481 , -0.30967801, ...\n",
      "X_test                    -> defaultdict(<class 'list'>, {0: array([[[ -6.40490\n",
      "X_test_std                -> defaultdict(<class 'list'>, {0: array([[ 0.1092488\n",
      "X_train                   -> array([[[ 0.00119031,  0.00873315,  0.00641749, ..\n",
      "X_train_std               -> array([[-0.74031227,  0.0126481 , -0.30967801, ...\n",
      "snrs                      -> [-20, -18, -16, -14, -12, -10, -8, -6, -4, -2, 0, \n",
      "y_16_val                  -> array([6, 6, 5, ..., 0, 4, 1])\n",
      "y_32_test                 -> defaultdict(<class 'list'>, {0: array([2, 2, 4, ..\n",
      "y_32_train                -> array([0, 3, 4, ..., 0, 3, 1])\n",
      "y_32_val                  -> array([2, 2, 4, ..., 0, 7, 3])\n",
      "y_test                    -> defaultdict(<class 'list'>, {0: array([6, 6, 5, ..\n",
      "y_train                   -> array([0, 3, 4, ..., 0, 3, 1])\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "from time import time\n",
    "import pickle  \n",
    "import sklearn\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "%store -r\n",
    "%store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data:  (80000, 32) and labels:  (80000,)\n",
      " \n",
      "Test data:\n",
      "Total 20 (4000, 32) arrays for SNR values:\n",
      "dict_keys([0, -16, 2, 4, 6, 8, 12, 10, -20, -14, -18, 16, 18, -12, 14, -10, -8, -6, -4, -2])\n"
     ]
    }
   ],
   "source": [
    "print(\"Training data: \", X_32train_std.shape, \"and labels: \", y_32_train.shape)\n",
    "print(\" \")\n",
    "print(\"Test data:\")\n",
    "print(\"Total\", len(X_32test_std), X_32test_std[18].shape, \"arrays for SNR values:\")\n",
    "print(X_32test_std.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and test the classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bagging: training took 18.12 seconds\n",
      "BaggingClassifier:\n",
      "BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "            presort=False, random_state=None, splitter='best'),\n",
      "         bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
      "         max_samples=100, n_estimators=400, n_jobs=1, oob_score=False,\n",
      "         random_state=None, verbose=0, warm_start=False)\n",
      " \n",
      "Pasting: training took 18.26 seconds\n",
      "PastingClassifier:\n",
      "BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "            presort=False, random_state=None, splitter='best'),\n",
      "         bootstrap=False, bootstrap_features=False, max_features=1.0,\n",
      "         max_samples=100, n_estimators=400, n_jobs=1, oob_score=False,\n",
      "         random_state=None, verbose=0, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "#Train the classifier\n",
    "\n",
    "bagg_clf = BaggingClassifier(DecisionTreeClassifier(), bootstrap=True, n_estimators=400, max_samples=100)\n",
    "paste_clf = BaggingClassifier(DecisionTreeClassifier(), bootstrap=False, n_estimators=400, max_samples=100)\n",
    "\n",
    "start = time()\n",
    "bagg_clf.fit(X_32train_std, y_32_train)  \n",
    "print(\"Bagging: training took %.2f seconds\"%(time() - start))\n",
    "print(\"BaggingClassifier:\")\n",
    "print(bagg_clf)\n",
    "\n",
    "print(\" \")\n",
    "\n",
    "start = time()\n",
    "paste_clf.fit(X_32train_std, y_32_train)  \n",
    "print(\"Pasting: training took %.2f seconds\"%(time() - start))\n",
    "print(\"PastingClassifier:\")\n",
    "print(paste_clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test bagging classifier\n",
      " \n",
      "Accuracy on -20 dB SNR samples =  0.118\n",
      "Accuracy on -18 dB SNR samples =  0.13075\n",
      "Accuracy on -16 dB SNR samples =  0.133\n",
      "Accuracy on -14 dB SNR samples =  0.131\n",
      "Accuracy on -12 dB SNR samples =  0.157\n",
      "Accuracy on -10 dB SNR samples =  0.17975\n",
      "Accuracy on -8 dB SNR samples =  0.245\n",
      "Accuracy on -6 dB SNR samples =  0.3015\n",
      "Accuracy on -4 dB SNR samples =  0.3405\n",
      "Accuracy on -2 dB SNR samples =  0.3925\n",
      "Accuracy on 0 dB SNR samples =  0.4735\n",
      "Accuracy on 2 dB SNR samples =  0.61725\n",
      "Accuracy on 4 dB SNR samples =  0.77425\n",
      "Accuracy on 6 dB SNR samples =  0.80825\n",
      "Accuracy on 8 dB SNR samples =  0.8065\n",
      "Accuracy on 10 dB SNR samples =  0.81475\n",
      "Accuracy on 12 dB SNR samples =  0.81075\n",
      "Accuracy on 14 dB SNR samples =  0.81275\n",
      "Accuracy on 16 dB SNR samples =  0.8075\n",
      "Accuracy on 18 dB SNR samples =  0.81\n"
     ]
    }
   ],
   "source": [
    "#Test the classifier\n",
    "\n",
    "import collections\n",
    "\n",
    "y_pred = defaultdict(list)\n",
    "accuracy_bagg = defaultdict(list)\n",
    "\n",
    "print(\"Test bagging classifier\")\n",
    "print(\" \")\n",
    "for snr in snrs:\n",
    "    y_pred[snr] = bagg_clf.predict(X_32test_std[snr])\n",
    "    accuracy_bagg[snr] = metrics.accuracy_score(y_32_test[snr], y_pred[snr])\n",
    "    print(\"Accuracy on %d dB SNR samples = \"%(snr), accuracy_bagg[snr])   \n",
    "    \n",
    "accuracy_bagg = collections.OrderedDict(sorted(accuracy_bagg.items()))  #sort by ascending SNR value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test pasting classifier\n",
      " \n",
      "Accuracy on -20 dB SNR samples =  0.1265\n",
      "Accuracy on -18 dB SNR samples =  0.1195\n",
      "Accuracy on -16 dB SNR samples =  0.1285\n",
      "Accuracy on -14 dB SNR samples =  0.12775\n",
      "Accuracy on -12 dB SNR samples =  0.16075\n",
      "Accuracy on -10 dB SNR samples =  0.18675\n",
      "Accuracy on -8 dB SNR samples =  0.24425\n",
      "Accuracy on -6 dB SNR samples =  0.2895\n",
      "Accuracy on -4 dB SNR samples =  0.361\n",
      "Accuracy on -2 dB SNR samples =  0.39125\n",
      "Accuracy on 0 dB SNR samples =  0.457\n",
      "Accuracy on 2 dB SNR samples =  0.605\n",
      "Accuracy on 4 dB SNR samples =  0.783\n",
      "Accuracy on 6 dB SNR samples =  0.817\n",
      "Accuracy on 8 dB SNR samples =  0.81725\n",
      "Accuracy on 10 dB SNR samples =  0.81625\n",
      "Accuracy on 12 dB SNR samples =  0.824\n",
      "Accuracy on 14 dB SNR samples =  0.8255\n",
      "Accuracy on 16 dB SNR samples =  0.8185\n",
      "Accuracy on 18 dB SNR samples =  0.81425\n"
     ]
    }
   ],
   "source": [
    "#Test the classifier\n",
    "\n",
    "import collections\n",
    "\n",
    "y_pred = defaultdict(list)\n",
    "accuracy_paste = defaultdict(list)\n",
    "\n",
    "print(\"Test pasting classifier\")\n",
    "print(\" \")\n",
    "for snr in snrs:\n",
    "    y_pred[snr] = paste_clf.predict(X_32test_std[snr])\n",
    "    accuracy_paste[snr] = metrics.accuracy_score(y_32_test[snr], y_pred[snr])\n",
    "    print(\"Accuracy on %d dB SNR samples = \"%(snr), accuracy_paste[snr])   \n",
    "    \n",
    "accuracy_paste = collections.OrderedDict(sorted(accuracy_paste.items()))  #sort by ascending SNR value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Visualize classifier performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0qFQq9W0fHx+LjAtv2bJltSehnjt3DpGRkYiJiUFycjLu378vmG2iqsq/J/fv31cPZQAq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x198181f1390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oFBg9enSNtZmIiIiILIdFFrl+fn7o06cPNm7ciHXr1mHfvn2YPXs2UlJSMHXq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\n+Jz/ACosDCpPD5eyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19819304a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the performance \n",
    "\n",
    "plt.style.use('classic')\n",
    "\n",
    "fig = plt.figure(figsize=(8, 4), dpi=100)\n",
    "x = snrs\n",
    "y = list(accuracy_bagg.values())\n",
    "plt.plot(x, y, marker=\"o\", linewidth=2.0, linestyle='dashed', color='royalblue')\n",
    "plt.axis([-20, 20, 0, 1])\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 2.0))\n",
    "plt.yticks(np.arange(0, 1, 0.10))\n",
    "\n",
    "ttl = plt.title('Bagging : SNR vs Accuracy', fontsize=16)\n",
    "ttl.set_weight('bold')\n",
    "plt.xlabel('SNR (dB)', fontsize=14)\n",
    "plt.ylabel('Test accuracy', fontsize=14)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()\n",
    "#----------------------------------------------------------\n",
    "\n",
    "plt.style.use('classic')\n",
    "\n",
    "fig = plt.figure(figsize=(8, 4), dpi=100)\n",
    "x = snrs\n",
    "y = list(accuracy_paste.values())\n",
    "plt.plot(x, y, marker=\"o\", linewidth=2.0, linestyle='dashed', color='royalblue')\n",
    "plt.axis([-20, 20, 0, 1])\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 2.0))\n",
    "plt.yticks(np.arange(0, 1, 0.10))\n",
    "\n",
    "ttl = plt.title('Pasting : SNR vs Accuracy', fontsize=16)\n",
    "ttl.set_weight('bold')\n",
    "plt.xlabel('SNR (dB)', fontsize=14)\n",
    "plt.ylabel('Test accuracy', fontsize=14)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
